library(tidyverse, verbose = F)
source("Funciones.R")
Lo datos corresponde a una matriz 5x5x10. La metadata será X = 1…25 y Z = 1…10 (profundidad), ambos detectores tienen la misma metadata.
##Metadata
Metadata <- data.frame(x = rep(1:100, each = 11),
z = rep(1:11, 100))
Comp.data <- data.frame(muestra = c("ss406", "ss407", "ss408", "ss409", "ss410"),
C = c(0.19,0.50,0.28,0.11,0.39),
Mn = c(0.53,0.13,0.64,0.48,0.43),
Ni = c(1.69,0.61,4.58,3.14,2.04),
Cr = c(2.12,3.00,0.09,1.22,1.72),
Mo = c(1.03,0.82,0.14,0.77,0.41),
Cu = c(0.32,0.43,0.73,0.23,0.47))
data.dir <- "Data/demon/"
wl <- read_tsv(paste(data.dir,"ss406.asc",sep = ""), col_names = F, progress = F, show_col_types = F) %>%
.[,1] %>% set_names("wl") %>% rowid_to_column()
## DEMON data
L_Demon <- map(c("ss406.asc","ss407.asc","ss408.asc","ss409.asc","ss410.asc"),
~ read_tsv(paste(data.dir,.x,sep = ""), col_names = F, progress = F, show_col_types = F))
L_Demon <- L_Demon %>% set_names(c("ss406","ss407","ss408","ss409","ss410"))
L_Demon <- L_Demon %>% map(~ .x %>% setNames(c("wl",paste("X", 1:(ncol(.x)-1), sep = ""))))
## elimina shot 1
L_Demon <- L_Demon %>%
map(~ .x %>% .[, c(1, which(Metadata$z != 1) + 1)]) ## elimina disparo #1 (limpieza)
g <- L_Demon %>% map(~ data.frame(.x[,1], Int = apply(.x[,2:ncol(.x)], 1, mean))) %>% bind_rows(.id = "id") %>%
ggplot(aes(x = wl, y = Int, color = id)) + geom_line() + labs(x = "Wavelength")
g %>% plotly::ggplotly()
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
## Normalizar
izq <- 267.70
der <- 267.75
int <- L_Demon %>% map_dfr(fun.int, izq = izq, der = der)
factor <- 1
int_norm <- int/factor
## acumular
int_norm <- sumar_filas_por_grupos(int_norm, n = 10, wl = F)
## Promediar
int_norm <- cbind(Comp.data, int_norm)
int_norm %>% pivot_longer(X1:X100, values_to = "Libs.Int") %>%
ggplot(aes(x = Libs.Int, y = Cr, color = muestra)) +
geom_point() +
geom_hline(yintercept = int_norm$Cr, lty = 2, col = "gray")
int_mean <- apply(int_norm %>% select(X1:X100), 1, promediar_grupos_aleatorios, n=10) %>% t() %>% data.frame()
int_mean <- cbind(Comp.data, int_mean)
int_mean %>% pivot_longer(X1:X10, values_to = "Libs.Int") %>%
ggplot(aes(x = Libs.Int, y = Cr, color = muestra)) +
geom_point() +
geom_hline(yintercept = int_norm$Cr, lty = 2, col = "gray")
int_mean %>% pivot_longer(X1:X10, values_to = "Libs.Int") %>%
ggplot(aes(x = Libs.Int, y = Cr, color = muestra)) +
geom_boxplot() +
geom_hline(yintercept = int_norm$Cr, lty = 2, col = "gray")
data.dir <- "Data/mechelle/"
## Mechelle data
L_Mechelle <- map(c("ss406.asc","ss407.asc","ss408.asc","ss409.asc","ss410.asc"),
~ read_tsv(paste(data.dir,.x,sep = ""), col_names = F, progress = F, show_col_types = F))
L_Mechelle <- L_Mechelle %>% map(~ .x %>% setNames(c("wl",paste("X", 1:(ncol(.x)-1), sep = ""))))
L_Mechelle <- L_Mechelle %>% set_names(c("ss406","ss407","ss408","ss409","ss410"))